Without a teacher, an RL agent learns from data collected by interacting with an initially unknown environment.
In general, the agent has to try new actions in novel states in order to discover potentially better policies.
Hence, it has to strike a good trade-off between exploitation (choosing good actions to maximize reward, based on information collected thus far) and exploration (choosing novel actions to discover potentially better alternatives), leading to the need for efficient exploration [1]}. In the context of dialog policy learning, the implication is that the policy learner actively tries new ways to converse with a user, in the hope of discovering a better policy in the long run. A recent survey is [2]}.
